Track accepted paper

CiteScore:
4.95ℹCiteScore:2017: 4.950CiteScore measures the average citations received per document published in this title. CiteScore values are based on citation counts in a given year (e.g. 2015) to documents published in three previous calendar years (e.g. 2012 – 14), divided by the number of documents in these three previous years (e.g. 2012 – 14).

Impact Factor:
3.962ℹImpact Factor:2017: 3.962The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
2017 Journal Citation Reports (Clarivate Analytics, 2018)

5-Year Impact Factor:
4.341ℹFive-Year Impact Factor:2017: 4.341To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years.
2017 Journal Citation Reports (Clarivate Analytics, 2018)

Source Normalized Impact per Paper (SNIP):
2.472ℹSource Normalized Impact per Paper (SNIP):2017: 2.472SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.

SCImago Journal Rank (SJR):
1.065ℹSCImago Journal Rank (SJR):2017: 1.065SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.

Author StatsℹAuthor Stats:Publishing your article with us has many benefits, such as having access to a personal dashboard: citation and usage data on your publications in one place. This free service is available to anyone who has published and whose publication is in Scopus.

Call for Papers

Text in scenes is an important source of information, since it conveys high-level semantics and is almost seen everywhere. These unique traits make scene text reading, involving automated detection and recognition of texts in scene images and videos, a very active research topic in the communities of computer vision, pattern recognition, and multimedia. Recently, these communities have observed a significant surge of research interests and efforts regarding the topic of Scene Text Reading, which is evidenced by the huge number of participants and submissions of ICDAR competitions as well as papers published on top journals and conferences.

Deep learning is now undisputed as the new de-facto method for solving a wide range of problems such as computer vision, speech recognition, natural language processing and reinforcement learning. The unifying idea behind such a vast success is the utilization of neural networks with many hidden layers, for the purposes of learning complex feature representations from raw data, rather than relying on handcrafted feature extraction methods. Ensemble learning, at the same time, has been widely employed to improve the performance of a single predictor. In the past decades we have witnessed remarkable progress in ensemble learning, starting from ensemble of simple models such as random forests to ensemble deep learning which dominates the ImageNet Large Scale Visual Recognition Challenges today.

Visual understanding is a fundamental cognitive ability in humans which is essential for identifying objects/people and interacting in social space. This cognitive skill makes interaction with the environment extremely effortless and provides an evolutionary advantage to humans as a species. In our daily routines, we, humans, not only learn and apply knowledge for visual recognition, we also have intrinsic abilities of transferring knowledge between related visual tasks, i.e., if the new visual task is closely related to the previous learning, we can quickly transfer this knowledge to perform the new visual task. In developing machine learning based automated visual recognition algorithms, it is desired to utilize these capabilities to make the algorithms adaptable.

The general question addressed by the special issue is the latest research results obtained through the interaction of bio / neuroscience and pattern recognition fields benefitting both research areas. The fundamental point of the special issue is to study and investigate how bio / neuroscience inspired systems, including hardware and software, deal with problems directly related to pattern recognition (e.g., deep learning, representation learning, transfer learning, multi-task learning, and unsupervised learning, spike neural network). We seek to include in the special issue recent successful studies on pattern recognition incorporating ideas and paradigms from the field of neuroscience. We also seek contributions from where neuroscience-inspired algorithms for pattern recognition still fall behind the state-of-the-art in terms of speed and accuracy. We also cover areas where deeper connections are likely to be fruitful. For example, we would like to highlight how neuroscience driven simulations (either hardware or software based) suggest new directions, which offer real advances for pattern recognition. Note that we are not interested in papers that focus on the details of such hardware or software, but on how they simulate pattern recognition, based on biological and neuro-scientific principles.

Representation learning has always been an important research area in pattern recognition. A good representation of practical data is critical to achieving satisfactory recognition performance. Broadly speaking, such presentation can be ``intra-data representation’’ or ``inter-data representation’’. Intra-data representation focuses on extracting or refining the raw feature of data point itself. Representative methods range from the early-staged hand-crafted feature design (e.g. SIFT, LBP, HoG, etc.), to the feature extraction (e.g. PCA, LDA, LLE, etc.) and feature selection (e.g. sparsity-based and submodulariry-based methods) in the past two decades, until the recent deep neural networks (e.g. CNN, RNN, etc.). Inter-data representation characterizes the relationship between different data points or the structure carried out by the dataset. For example, metric learning, kernel learning and causality reasoning investigate the spatial or temporal relationship among different examples, while subspace learning, manifold learning and clustering discover the underlying structural property inherited by the dataset.
Above analyses reflect that representation learning covers a wide range of research topics related to pattern recognition. On one hand, many new algorithms on representation learning are put forward every year to cater for the needs of processing and understanding various practical data. On the other hand, massive problems regarding representation learning still remain unsolved, especially for the big data and noisy data. Thereby, the objective of this special issue is to provide a stage for researchers all over the world to publish their latest and original results on representation learning.

Smart and Autonomous Systems (SAS) require minimal or no human operator intervention. Examples include robotic platforms, networked systems that combine computing, sensing, communication, and actuation, amongst others. They exhibit a high-level of awareness beyond primitive actions, in support of persistent and long-term autonomy. They employ a variety of representation and reasoning mechanisms, such as semantic or probabilistic reasoning, decision-making in uncertainties, and intention inference of other entities in their vicinity.

Computer aided cancer detection and diagnosis (CAD) has made significant strides in the past 10 years, with the result that many successful CAD systems have been developed. However, the accuracy of these systems still requires significant improvement, so that the can meet the needs of real world diagnostic situations.. Recent progress in machine learning offers new prospects for computer aided cancer detection and diagnosis. A major recent development is the massive success resulting from the use of deep learning techniques, which has attracted attention from both the academic research and commercial application communities. Deep learning is the fastest-growing field in machine learning and is widespread uses in cancer detection and diagnosis. Recent research has demonstrated that deep learning can increase cancer detection accuracy significantly. Thus, deep learning techniques offer the promise not only of more accurate CAD systems for cancer detection and diagnosis, but may also revolutionize their design.

This SI invites contributions which make novel developments to the theory and application of pattern recognition and machine learning to the analysis of human motion and deformable objects. Articulated motion and deformable objects (AMDO) research focuses on the automatic analysis of complex objects, such as the human body. The subject is important to different fields, including pattern recognition, computer vision, computer graphics, multimedia applications, and multimodal interfaces. Advances in the automatic analysis of this kind of objects will promote the generation of new technologies and applications in many sectors, including leisure industry (gaming, intelligent retrieval of video data, augmented reality, Human Computer Interaction, etc.), security (security surveillance and ambient intelligence), health care (greater autonomy for those suffering disabling diseases, advanced assisted living, inpatient monitoring, supported diagnosis, etc.) and energy (smart rooms, buildings and cities), to name just a few. This Special Issue invites extended and updated versions of papers published at recent AMDO conferences as well as submissions from anybody presenting novel Pattern Recognition methods in the field of AMDO.

Machine learning techniques have played a central role in pattern recognition, and a variety of machine learning methods have been developed for various pattern recognition applications over the past decade. Among these learning methods, distance metric learning has achieved many state-of-the-arts in many pattern recognition applications, which aims to learn an appropriate distance function given some constrains between samples. To better discover the geometric property of high-dimensional feature spaces and exploit the complementary information of different feature spaces, manifold learning and multi-view learning strategies have also been integrated into distance metric learning to further improve the performance of various distance metric learning methods. While these methods are helpful to learn the similarity of data such as images, videos, texts, radars, and voices, how to develop task-specific distance metric learning algorithms for different pattern recognition tasks still remains unsolved, especially for big data which are captured in the wild. Moreover, how to develop transferable and nonlinear distance metric learning methods for large-scale pattern recognition systems still requires many efforts.

We are living in a world where we are surrounded by so many intelligent video-capturing devices. These devices capture data about how we live and what we do. For example, thanks to surveillance and action cameras, as well as smart phones and even old-fashioned camcorders, we are able to record videos at an unprecedented scale and pace. There is exceedingly rich information and knowledge embedded in all those videos. With the recent advances in computer vision, we now have the ability to mine such massive visual data to obtain valuable insight about what is happening in the world. Due to the remarkable successes of deep learning techniques, we are now able to boost video analysis performance significantly and initiate new research directions to analyze video content. For example, convolutional neural networks have demonstrated superiority on modeling high-level visual concepts, while recurrent neural networks have shown promise in modeling temporal dynamics in videos. Deep video analytics, or video analytics with deep learning, is becoming an emerging research area in the field of pattern recognition.